a Novel Approach for Image Enhancement by Using Contrast Limited Adaptive Histogram Equalization Method

8/17/2019 a Novel Approach for Image Enhancement by Using Contrast Limited Adaptive Histogram Equalization Method

http://slidepdf.com/reader/full/a-novel-approach-for-image-enhancement-by-using-contrast-limited-adaptive-histogram 1/6

1

A Novel Approach for Image Enhancement by Using

Contrast Limited Adaptive Histogram Equalization

Method

S.Muniyappan Dr.A.Allirani S.Saraswathi

Assistant Professor Principal Assistant Professor

Department of Computer Science & Engineering SRS College of Engineering & Technology Department of ECE

SRS College of Engineering and Technology Salem.122 mahendra engineering college

Salem.122 Namakkal

[email protected] [email protected] [email protected]

Abstract

A novel approach for image enhancement by using contrast

limited adaptive histogram equalization method will

produces a good contrast images such as medical images. In

this paper, we propose a new method for image

enhancement by using contrast limited adaptive histogram

equalization method. We propose a general framework

with a adaptive histogram equalization method. We are

going to prove its effectiveness in comparison to other

contrast enhancement method.

Index Terms - histogram equalization, histogram

smoothing, Adaptive histogram equalization. Contrast

Limited Adaptive Histogram Equalization.

I. INTRODUCTION

The purpose of image enhancement is to produce the

important procedure of image processing, this procedure

are to edit the original image to be more look enhanced

contrast for a specific application. The Contrast

enhancement technique will play an important role in

image processing applications, such as mobile images,

digital photographs, and analysis of medical images,

remote sensing, and various scientific images. All the

images should be have several reasons to be providing

poor contrast for an image because they may be use poor

quality of the imaging device or lighting of climate so we

propose a new contrast enhancement technique was

aimed at to eliminate these types of problems. Different

contrast enhancement techniques are used to improve the

contrast of an image such as histogram equalization,

histogram modification, greedy algorithm, adaptive

histogram equalization etc. This paper presents here a

new approach for contrast enhancement based upon

contrast limited adaptive histogram equalization method.

II. EXITING CONTRAST ENHANCEMENT

METHOD:

Today many contrast enhancement techniques are

available they are produce unclear images so we are first

discuss the few contrast enhancement t techniques. They

are following

a) Histogram equalization

The goal of histogram equalization is to distribute the gray

levels within an image so that every gray level is equally

likely to occur. Histogram equalization will increase the

brightness and contrast of a dark and low contrast images.

Making features observable that was not visible in the

original image. It also used to standardize the brightness

and contrast of images the process of histogram

equalization is to find a mapping function that maps the

image input histogram function to the uniformly

distributed output histogram function. Histogram

IEEE - 31661

4th ICCCNT - 13

July 4 - 6, 2013, Tiruchengode, India



2

equalization also seems to be used in biological neural

networks so as to maximize the output firing rate of the

neuron as a function of the input statistics. This has beenproved in particular in the fly retina.[5] Histogram

equalization is a specific case of the more general class of

histogram remapping methods. These methods seek to

adjust the image to make it easier to analyze or improve

visual quality

b) Histogram Smoothing

To avoid spikes that lead to strong repelling fixed points

a smoothness constraint can be add the goal. The

backward variance of the histogram is used to measuring

the smoothness. A smooth can be modify the histogram

will tend to have fewer spikes since they are essentially

abrupt changes in the histogram.

c) Adaptive Histogram Equalization:

Ordinary histogram equalization uses the same

transformation derived from the image histogram to

transform all pixels. This works well when the

distribution of pixel values is similar throughout the

image. However, when the image contains regions that

are significantly lighter or darker than most of the image,

the contrast in those regions will not be sufficiently

enhanced. Adaptive histogram equalization (AHE)

improves on this by transforming each pixel with a

transformation function derived from a neighbourhood

region. It was first developed for use in aircraft cockpit

displays.[1] cited in [2].. When the image region containing a

pixel's neighbourhood is fairly homogeneous, its

histogram will be strongly peaked, and the transformation

function will map a narrow range of pixel values to the

whole range of the result image. This causes AHE to over

amplify small amounts of noise in largely homogeneous

regions of the image.[4].. This method used to improve

the contrast of the images. It varies from histogram

equalization with

respect that the adaptive method make

the calculation of the several histograms, every

corresponding to a different section of the image, and use

to reallocate the lightness values of the image. It istherefore convenient for to increase the local contrast of

an image and convey out more detail.

III. PROPOSED ALGORITHM

A. Contrast Limited Adaptive Histogram

Equalization

A proposed algorithm was specially developed by

medical images and it provides a good enhanced image

better of original images. The CLAHE algorithm

partitions the images into contextual regions and applies

the histogram equalization to each one. These evens

produce the distribution of used grey values and thus

make hidden features of the image more visible. CLAHE

is an improved algorithme of AHE. We have enhance The

test images by using proposed algorithm, histogram

equalization, histogram smoothing, Adaptive histogram

equalization & enhanced with contrast limited adaptive

histogram. These mentioned enhancement techniques

produced following results for the above images Figure 1,

represents visual results for the first test image (breast

cancer). In visual analysis it is observed that contrast has

been enhanced to various levels by all the algorithms but

the proposed algorithm is enhancing the image more

precisely in comparison to contrast limited adaptive

histogram. Histogram equalization, histogram smoothing,

Adaptive histogram equalization.

Fig 1. a) Original image b) Histogram equalization

IEEE - 31661

4th ICCCNT - 13




3

c) Histogram smoothing d) Adaptive histogram

equalization

e) Contrast Limited Adaptive Histogram Equalization

The human visualization is not considered as benchmark

for image quality, so to estimate the accomplishment of

above mentioned algorithms quality metrics have been

calculated for the output images to from the original

image. the bellow images Figure 2 represents the

mapping of enhanced images histogram level

Fig 2. a) Original image b) Histogram equalization

c) Histogram smoothing d) Adaptive histogram

equalization


The evaluation of Proposed Enhancement technique

produces better quality values for enhanced image.

Following table1 represents the comparison of CLAHE

with others. The derived results are again giving better

values to Proposed Enhancement method followed by

Adaptive Enhancement. The exiting method are also

producing images having quality values, but less good

than Contrast Limited Adaptive Histogram Equalization

Different methods Contrast level

Histogram equalization 220

Histogram smoothing 248

Adaptive histogram equalization. 250

Contrast Limited Adaptive Histogram

Equalization

260

Table 1 comparison of CLAHE with exiting methods

IEEE - 31661

4th ICCCNT - 13




4

ALGORITHM STEPS:

Fig 3. Flow chart of Contrast Limited Adaptive Histogram

Equalization algorithm

1. Obtain the inputs: Specifies the number of tile rows

and columns and set the clip Limit and number of bins

for the histogram used in building a contrast enhancing

transformation. Higher values result in greater dynamic

range at the cost of slower processing speed. Clip limit

for contrast Enhancement technique is normalized from 0

to 1 limits contrast enhancement. Higher numbers get the

result in more contrast.

2. Processing the inputs: specifies the real clip limit

from the normalized value if necessary, pad the image

before splitting it into regions

3. Process each row and columns region (tile) thus

producing gray level mappings and make a histogram

for this region using the specified number of bins, clip

the histogram using clip limit, the contrast limiting

procedure has to be applied for each neighbourhood from

which a transformation function is derived.

CLAHE wasdeveloped[3] to prevent the over amplification of noise

that adaptive histogram equalization can give rise to.

4. Interpolation allows a significant improvement in

efficiency without compromising the quality of the result

Fig 4. a) CLAHE with Clip Limit= 0.11

b) CLAHE with Clip Limit= 0.22

the above figure 4 represent the Contrast Limited

Adaptive Histogram Equalization with various clip limit

level. The Clip limit for CLAHE is normalized from 0 to

IEEE - 31661

4th ICCCNT - 13




5

1 limits contrast enhancement. Higher numbers get the

result in more contrast. The following table1.1 represents

the performance of the CLAHE with various clip limitand contrast level of the output image

CLAHE with Clip Limit Contrast level

0.11 258

0.18 260

0.22 265

Table1.1 Performance of the CLAHE with Various Clip

Limit

Fig 5. a) Original image and mapping

b) Histogram smoothing & mapping for the enhanced

image

c) Histogram equalization & mapping for the enhanced

image

d) Adaptive histogram equalization and mapping


and mapping for the enhanced image

IV. CONCLUSION

In this paper, contrast limited adaptive histogram

equalization approach for contrast enhancement has been

proposed for breast cancer images. on comparing this

approach with the existing popular approaches of

Histogram equalization, histogram smoothing, Adaptive

histogram equalization it has been concluded that the

proposed technique is giving much better results than the

existing ones.

V. REFERENCES

[1] D. J. Ketcham, R. W. Lowe & J. W. Weber: Image

enhancement techniques for cockpit displays. Tech. rep.,

Hughes Aircraft. 1974

[2] R.A. Hummel: Image Enhancement by Histogram

Transformation. Computer Graphics and Image

Processing 6 (1977) 184195.

IEEE - 31661

4th ICCCNT - 13




6

[3] S. M. Pizer, E. P. Amburn, J. D. Austin, et

al.: Adaptive Histogram Equalization and Its Variations.

Computer Vision, Graphics, and Image Processing 39

(1987) 355-368.

[4] K.Zuiderveld: Contrast Limited Adaptive Histogram

Equalization. In: P. Heckbert: Graphics Gems IV,

Academic Press 1994, ISBN 0-12-336155-9

[5] Acharya and Ray, Image Processing: Principles and

Applications, Wiley-Interscience 2005 ISBN

0-471-71998-6

[6] R. C. Gonzalez and R. E.Woods, Digital Image

Processing. New York:Addison-Wesley, 1992.

[7] M. A. Sid-Ahmed, Image Processing: Theory,

Algorithms, and Archi-tectures. New York:

McGraw-Hill, 1995, ch. 4.

IEEE - 31661

4th ICCCNT - 13


Documents

a Novel Approach for Image Enhancement by Using Contrast Limited Adaptive Histogram Equalization Method